OpenRaiser/Intern-Atlas huggingface dataset:
Monte Carlo Methods for the Game Kingdomino
Venue: arXiv cs.AI 2018
Year: 2018

Kingdomino is introduced as an interesting game for studying game playing: the game is multiplayer (4 independent players per game); it has a limited game depth (13 moves per player); and it has limited but not insignificant interaction among players. Several strategies based on locally greedy players, Monte Carlo Evaluation (MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We examine a variation of UCT called progressive win bias and a playout policy (Player-greedy) focused on selecting good moves for the player. A thorough evaluation is done showing how the strategies perform and how to choose parameters given specific time constraints. The evaluation shows that surprisingly MCE is stronger than MCTS for a game like Kingdomino. All experiments use a cloud-native design, with a game server in a Docker container, and agents communicating using a REST-style JSON protocol. This enables a multi-language approach to separating the game state, the strategy implementations, and the coordination layer.
Prostate Segmentation using 2D Bridged U-net
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we focus on three problems in deep learning based medical image segmentation. Firstly, U-net, as a popular model for medical image segmentation, is difficult to train when convolutional layers increase even though a deeper network usually has a better generalization ability because of more learnable parameters. Secondly, the exponential ReLU (ELU), as an alternative of ReLU, is not much different from ReLU when the network of interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss functions for medical image segmentation, is not effective when the prediction is close to ground truth and will cause oscillation during training. To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size. Meanwhile, we propose a new loss function to accelerate the learning process and a combination of different activation functions to improve the network performance. Our experimental results suggest that our network is comparable or superior to state-of-the-art methods.
A new graph modelisation for molecule similarity
Venue: arXiv cs.DS 2018
Year: 2018

In order to define the process of restrosynthesis of a new organic molecule, it is often necessary to be able to draw inspiration from that of a molecule similar to the target one of which we know such a process. To compute such a similarity, an oftently used approach is to solve a Maximum Common Edge Subgraph (MCES) problem on molecular graphs, but such an approach is limited by computation time and pertinence of similarity measurement. In this paper, we define and analyse here a new graph representation of molecules to algorithmically compare them. The purpose is to model the structure of molecule by a graph smaller than the molecular graph and representing the interconnexion of its elementary cycles. We provide an algorithm to efficiently obtain such a graph of cycles from a molecular graph. Then by solving MCES problems on those graphs, we evaluate the pertinence of using graphs of cycles for molecular similarity on a select set of molecules.
Proofs and Performance Evaluation of Greedy Multi-Channel Neighbor Discovery Approaches
Venue: arXiv cs.NI 2018
Year: 2018

The accelerating penetration of physical environments by objects with information processing and wireless communication capabilities requires approaches to find potential communication partners and discover services. In the present work, we focus on passive discovery approaches in multi-channel wireless networks based on overhearing periodic beacon transmissions of neighboring devices which are otherwise agnostic to the discovery process. We propose a family of low-complexity algorithms that generate listening schedules guaranteed to discover all neighbors. The presented approaches simultaneously depending on the beacon periods optimize the worst case discovery time, the mean discovery time, and the mean number of neighbors discovered until any arbitrary in time. The presented algorithms are fully compatible with technologies such as IEEE 802.11 and IEEE 802.15.4. Complementing the proposed low-complexity algorithms, we formulate the problem of computing discovery schedules that minimize the mean discovery time for arbitrary beacon periods as an integer linear problem. We study the performance of the proposed approaches analytically, by means of numerical experiments, and by extensively simulating them under realistic conditions. We observe that the generated listening schedules significantly - by up to factor 4 for the mean discovery time, and by up to 300% for the mean number of neighbors discovered until each point in time - outperform the Passive Scan, a discovery approach defined in the IEEE 802.15.4 standard. Based on the gained insights, we discuss how the selection of the beacon periods influences the efficiency of the discovery process, and provide recommendations for the design of systems and protocols.
Optimal Network Topology for Effective Collective Response
Venue: arXiv cs.SY 2018
Year: 2018

Natural, social, and artificial multi-agent systems usually operate in dynamic environments, where the ability to respond to changing circumstances is a crucial feature. An effective collective response requires suitable information transfer among agents, and thus is critically dependent on the agents' interaction network. In order to investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making---the leader-follower linear consensus---and study the collective capacity of the system to follow a dynamic driving signal (the "leader") for a range of topologies and system sizes. The analysis reveals a nontrivial relationship between optimal topology and frequency of the driving signal. Interestingly, the response is optimal when each individual interacts with a certain number of agents which decreases monotonically with the frequency and, for large enough systems, is independent of the size of the system. This phenomenology is investigated in experiments of collective motion using a swarm of land robots. The emergent collective response to both a slow- and a fast-changing leader is measured and analyzed for a range of interaction topologies. These results have far-reaching practical implications for the design and understanding of distributed systems, since they highlight that a dynamic rewiring of the interaction network is paramount to the effective collective operations of multi-agent systems at different time-scales.
Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods
Venue: arXiv cs.LG 2018
Year: 2018

The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecasting of forthcoming positions). This paper focuses on predictive analytics for moving objects (could be pedestrians, cars, vessels, planes, animals, etc.) and surveys the state-of-the-art in the context of future location and trajectory prediction. We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects. We also list the properties of several real datasets used in the past for validation purposes of those works and, motivated by this, we discuss challenges that arise in the transition from conventional to Big Data applications. CCS Concepts: Information systems > Spatial-temporal systems; Information systems > Data analytics; Information systems > Data mining; Computing methodologies > Machine learning Additional Key Words and Phrases: mobility data, moving object trajectories, trajectory prediction, future location prediction.
Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates
Venue: arXiv cs.CV 2018
Year: 2018

In many retrieval problems, where we must retrieve one or more entries from a gallery in response to a probe, it is common practice to learn to do by directly comparing the probe and gallery entries to one another. In many situations the gallery and probe have common covariates -- external variables that are common to both. In principle it is possible to perform the retrieval based merely on these covariates. The process, however, becomes gated by our ability to recognize the covariates for the probe and gallery entries correctly. In this paper we analyze optimal strategies for retrieval based only on matching covariates, when the recognition of the covariates is itself inaccurate. We investigate multiple problems: recovering one item from a gallery of $N$ entries, matching pairs of instances, and retrieval from large collections. We verify our analytical formulae through experiments to verify their correctness in practical settings.
Modeling, Analysis, and Hard Real-time Scheduling of Adaptive Streaming Applications
Venue: arXiv cs.DC 2018
Year: 2018

In real-time systems, the application's behavior has to be predictable at compile-time to guarantee timing constraints. However, modern streaming applications which exhibit adaptive behavior due to mode switching at run-time, may degrade system predictability due to unknown behavior of the application during mode transitions. Therefore, proper temporal analysis during mode transitions is imperative to preserve system predictability. To this end, in this paper, we initially introduce Mode Aware Data Flow (MADF) which is our new predictable Model of Computation (MoC) to efficiently capture the behavior of adaptive streaming applications. Then, as an important part of the operational semantics of MADF, we propose the Maximum-Overlap Offset (MOO) which is our novel protocol for mode transitions. The main advantage of this transition protocol is that, in contrast to self-timed transition protocols, it avoids timing interference between modes upon mode transitions. As a result, any mode transition can be analyzed independently from the mode transitions that occurred in the past. Based on this transition protocol, we propose a hard real-time analysis as well to guarantee timing constraints by avoiding processor overloading during mode transitions. Therefore, using this protocol, we can derive a lower bound and an upper bound on the earliest starting time of the tasks in the new mode during mode transitions in such a way that hard real-time constraints are respected.
CADDY Underwater Stereo-Vision Dataset for Human-Robot Interaction (HRI) in the Context of Diver Activities
Venue: arXiv cs.CV 2018
Year: 2018

In this article we present a novel underwater dataset collected from several field trials within the EU FP7 project "Cognitive autonomous diving buddy (CADDY)", where an Autonomous Underwater Vehicle (AUV) was used to interact with divers and monitor their activities. To our knowledge, this is one of the first efforts to collect a large dataset in underwater environments targeting object classification, segmentation and human pose estimation tasks. The first part of the dataset contains stereo camera recordings (~10K) of divers performing hand gestures to communicate and interact with an AUV in different environmental conditions. These gestures samples serve to test the robustness of object detection and classification algorithms against underwater image distortions i.e., color attenuation and light backscatter. The second part includes stereo footage (~12.7K) of divers free-swimming in front of the AUV, along with synchronized IMUs measurements located throughout the diver's suit (DiverNet) which serve as ground-truth for human pose and tracking methods. In both cases, these rectified images allow investigation of 3D representation and reasoning pipelines from low-texture targets commonly present in underwater scenarios. In this paper we describe our recording platform, sensor calibration procedure plus the data format and the utilities provided to use the dataset.
Detection and Mitigation of Classes of Attacks in Supervisory Control Systems
Venue: arXiv cs.FL 2018
Year: 2018

The deployment of control systems with network-connected components has made feedback control systems vulnerable to attacks over the network. This paper considers the problem of intrusion detection and mitigation in supervisory control systems, where the attacker has the ability to enable or disable vulnerable actuator commands and erase or insert vulnerable sensor readings. We present a mathematical model for the system under certain classes of actuator enablement attacks, sensor erasure attacks, or sensor insertion attacks. We then propose a defense strategy that aims to detect such attacks online and disables all controllable events after an attack is detected. We develop an algorithmic procedure for verifying whether the system can prevent damage from the attacks considered with the proposed defense strategy, where damage is modeled as the reachability of a pre-defined set of unsafe system states.The technical condition of interest that is necessary and sufficient in this context, termed "GF-safe controllability", is characterized. We show that the verification of GF-safe controllability can be performed using diagnoser or verifier automata. Finally, we illustrate the methodology with a traffic control system example.
TequilaGAN: How to easily identify GAN samples
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data. We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples. We provide results on MNIST, CIFAR10, music and speech data.
No-regret algorithms for online $k$-submodular maximization
Venue: arXiv cs.DS 2018
Year: 2018

We present a polynomial time algorithm for online maximization of $k$-submodular maximization. For online (nonmonotone) $k$-submodular maximization, our algorithm achieves a tight approximate factor in an approximate regret. For online monotone $k$-submodular maximization, our approximate-regret matches to the best-known approximation ratio, which is tight asymptotically as $k$ tends to infinity. Our approach is based on the Blackwell approachability theorem and online linear optimization.
Envision of an integrated information system for project-driven production in construction
Venue: arXiv cs.HC 2018
Year: 2018

Construction frequently appears at the bottom of productivity charts with decreasing indexes of productivity over the years. Lack of innovation and delayed adoption, informal processes or insufficient rigor and consistency in process execution, insufficient knowledge transfer from project to project, weak project monitoring, little cross- functional cooperation, little collaboration with suppliers, conservative company culture, and a shortage of young talent and people development are usual issues. Whereas work has been carried out on information technology and automation in construction their application is isolated without an interconnected information flow. This paper suggests a framework to address production issues on construction by implementing an integrated automatic supervisory control and data acquisition for management and operations. The system is divided into planning, monitoring, controlling, and executing groups clustering technologies to track both the project product and production. This research stands on the four pillars of manufacturing knowledge and lean production (production processes, production management, equipment/tool design, and automated systems and control). The framework offers benefits such as increased information flow, detection and prevention of overburdening equipment or labor (Muri) and production unevenness (Mura), reduction of waste (Muda), evidential and continuous process standardization and improvement, reuse and abstraction of project information across endeavors.
Maintaning maximal matching with lookahead
Venue: arXiv cs.DS 2018
Year: 2018

In this paper we study the problem of fully dynamic maximal matching with lookahead. In a fully dynamic $n$-vertex graph setting, we have to handle updates (insertions and removals of edges), and answer queries regarding the current graph, preferably with a better time bound than that when running the trivial deterministic algorithm with worst-case time of $O(m)$ (where $m$ is the all-time maximum number of the edges) and recompute the matching from scratch each time a query arrives. We show that a maximal matching can be maintained in an (undirected) general graph with a deterministic amortized update cost of $O(\log m)$, provided that a lookahead of length $m$ is available, i.e. we can ``take a peek'' at the next $m$ update operations in advance.
A Tight Lower Bound for Clock Synchronization in Odd-Ary M-Toroids
Venue: arXiv cs.DC 2018
Year: 2018

Synchronizing clocks in a distributed system in which processes communicate through messages with uncertain delays is subject to inherent errors. Prior work has shown upper and lower bounds on the best synchronization achievable in a variety of network topologies and assumptions about the uncertainty on the message delays. However, until now there has not been a tight closed-form expression for the optimal synchronization in $k$-ary $m$-cubes with wraparound, where $k$ is odd. In this paper, we prove a lower bound of $\frac{1}{4}um\left(k-\frac{1}{k}\right)$, where $k$ is the (odd) number of processes in the each of the $m$ dimensions, and $u$ is the uncertainty in delay on every link. Our lower bound matches the previously known upper bound.
Large-Scale Visual Speech Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Venue: arXiv cs.RO 2018
Year: 2018

Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as 'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.
Generating Synthetic Data for Neural Keyword-to-Question Models
Venue: arXiv cs.IR 2018
Year: 2018

Search typically relies on keyword queries, but these are often semantically ambiguous. We propose to overcome this by offering users natural language questions, based on their keyword queries, to disambiguate their intent. This keyword-to-question task may be addressed using neural machine translation techniques. Neural translation models, however, require massive amounts of training data (keyword-question pairs), which is unavailable for this task. The main idea of this paper is to generate large amounts of synthetic training data from a small seed set of hand-labeled keyword-question pairs. Since natural language questions are available in large quantities, we develop models to automatically generate the corresponding keyword queries. Further, we introduce various filtering mechanisms to ensure that synthetic training data is of high quality. We demonstrate the feasibility of our approach using both automatic and manual evaluation. This is an extended version of the article published with the same title in the Proceedings of ICTIR'18.
Generalization in quasi-periodic environments
Venue: arXiv cs.LG 2018
Year: 2018

By and large the behavior of stochastic gradient is regarded as a challenging problem, and it is often presented in the framework of statistical machine learning. This paper offers a novel view on the analysis of on-line models of learning that arises when dealing with a generalized version of stochastic gradient that is based on dissipative dynamics. In order to face the complex evolution of these models, a systematic treatment is proposed which is based on energy balance equations that are derived by means of the Caldirola-Kanai (CK) Hamiltonian. According to these equations, learning can be regarded as an ordering process which corresponds with the decrement of the loss function. Finally, the main results established in this paper is that in the case of quasi-periodic environments, where the pattern novelty is progressively limited as time goes by, the system dynamics yields an asymptotically consistent solution in the weight space, that is the solution maps similar patterns to the same decision.
A Geo-Aware Server Assignment Problem for Mobile Edge Computing
Venue: arXiv cs.DC 2018
Year: 2018

As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity servers are deployed in the edge to form a decentralized network of mini datacenters each serving a localized region. A challenge is how to place these servers geographically to maximize the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets.